For many companies, it is desirable to forecast the financial future of a product or service offered by the company. For example, a company may want to know the approximate change in future sales, growth, and profit of the product or service. In addition, many companies want to know the effects of changes in causal events (e.g., marketing, advertising, and/or pricing changes) on forecasted data (e.g., sales volume, growth, and/or profit) and how these changes affect future of the offered product or service. As a result, many companies use a forecast model encompassing many pieces of causal data to predict forecasted data.
In modifying the input values in existing forecast models, each causal event must be modified individually (e.g., raising the number of TV ratings points, number of radio ad spots, circulation, etc.). In addition, each event has a different unit of measure. Therefore, a large amount of time must be spent adjusting each causal even value for resimulation of a forecast model. Furthermore, understanding of the interrelation between the causal values is crucial when modifying. Hence, errors could easily occur in modifying inputs to a forecast model.
Therefore, what is needed is a forecast model that allows a user a coarser granularity of measurement and modification of causal values to a forecast model.